Shopping experiences for retailers has become increasingly done online via computers and mobile devices such as smartphones. To meet this demand, many retailers offer their products for sale online with other useful information such as store locations, hours, and inventory data. Thus, it is common for people to search online for a particular store location or product. For example, when searching for a particular product, a user may type in a query such as a text string in an attempt to find a matching product. Typically, the retailer's website platform receives this search query, executes the query to find several matches within a database, and returns the search results to the user so they can be viewed.
However, traditional search platforms provide the same search results for the same search queries, regardless of whether the returned search results may be relevant to each particular user. In other words, traditional searches simply match search queries to a database regardless of when the search is performed and who is doing the searching, and do not take other parameters into account that may influence the relevancy of the returned search results. As a result, typical search results provided for one user may be more or less relevant than those same search results for another user. Because more relevant search results are more likely to result in conversions, i.e., search queries that result in the customer actually making a purchase, current search processes are ineffective.